(330d) Computer Aided Flowsheet Design Using a Group Contribution Based Approach

There are many different approaches to process synthesis including expert systems, optimization or algorithmic methods, and conceptual methods based on physical insights. This paper highlights a novel hybrid method for Computer Aided Flowsheet Design (CAFD) that combines physical insights with algorithmic reverse design approaches to enable systematic identification of feasible flowsheets at significantly reduced computational expense.

The framework presented in this paper is based on the process group (PG) contribution approach developed by d’Anterroches and Gani (2005). The CAFD approach was inspired by the group contribution based methods for Computer Aided Molecular Design (CAMD), which includes building blocks (atoms and functional groups) to generate and represent molecules; group contribution (GC) based property models to predict target properties; a standard molecular structure notation system (such as SMILES) to store and visualize the molecular structure information; and a synthesis method to generate and screen molecules that match the target (design) properties. Analogous to CAMD, in the CAFD approach, flowsheets are generated and represented by functional process groups; process group contribution based property models are employed to predict flowsheet properties; a notation system (called SFILES) is used for storing the flowsheet structural information; and a synthesis method is used to generate and identify the feasible flowsheets.

Like functional groups in molecules that are characterized by atoms and their molecular weight, each process group is characterized by the type of unit operation/process and their corresponding driving force. Each PG contributes to the flowsheet (performance) properties, which can be calculated once a feasible configuration has been identified. The candidate flowsheets are ranked based on performance criteria like energy consumption, amount (mass) of external agents used and/or cost/profit. Once a set of near optimal flowsheet alternatives have been identified, rigorous simulation is used to verify the predicted performance and select the best option.